Legal System Construction for Energy Industry in China

In a very real sense,China did not have a legal system of energy until 1978 when the policy of reform and opening-up was carried out.Over the 30 years since then,China has achieved great accomplishments in energy development,which have attracted worldwide attention,

Measurement and Strategy Research on the Green Development Level for the New Energy Vehicle Industry in Chongqing City

This article takes 2016-2022 as the inspection period to construct an evaluation index system for the green development level of the new energy vehicle industry.The entropy method and comprehensive index are used to measure the green development level of the new energy vehicle industry in Chongqing,and compared with neighboring provinces such as Yunnan,Guizhou,and Sichuan.Policy recommendations are proposed to promote the development of the new energy vehicle industry in Chongqing City.

Current Status and Prospects of Biomass Energy Industry in China

At present biomass energy industry is in its infancy in China and it has a bright future. Biomass energy production used grain as raw materials has entered industrialization phase.Some key technologies of biomass energy industry are coming to mature.China has issued relevant industrial standards laws and regulations,and has provided support in finance,loan,tax,etc.But China's biomass energy industry is faced with many problems which need to be solved.For example,taking grain as raw materials is unsustain...

Global trends and development of energy services industry in China

Energy service is an effective way to promote energy conservation by market mechanisms,including energy saving services,energy procurement,supply of many varieties of energy,supply of renewable energy technologies,energy-related consulting services,risk management,etc.China is a major energy consumer but energy is in short supply,and the efficiency of energy use is low.China's energy service industry has expanded rapidly,in terms of both the number of new Energy Service Companies entering the market and amount of capital invested in Energy Performance Contracting projects,but the energy service sector in China is still at an early stage of development.Developed countries began early in developing the energy service sector and their energy service market is mature,and the experience of developed countries shows that energy services play a significant role in advancing energy saving and emission reduction.Under the new situation,China needs combine energy services experience of developed countries,and take following measures to accelerate China's energy services rapid and healthy development,including the long-term aspects of policy planning,energy-saving core technology,finance and capital investment,public sector reductions,personnel training,and so on.

Discount Rate of China’s New Energy Power Industry

Under the dual pressures of energy crisis and environmental pollution,China’s new energy power industry has become a focal point for environmental management and requires greater investment.In this context,as a significant input of investment projects,discount rate requires a well-calibrated evaluation because new energy power investment projects are highly capital intensive.The main objective of this paper is to evaluate the discount rate of China’s new energy power industry.First,we use Moving Average to correct the parameters of capital asset pricing model(CAPM)and weighted average cost of capital,which extends the literature on the avoidance of CAPM noise information problem.Second,we study the industry-level annual discount rates of mainly China’s new energy power industries,including hydropower,nuclear power,wind power,and photovoltaic power industries for the period of 2014-2019.The results show that discount rates in China’s new energy power industries evolved differently between the years of 2014-2019 with average annual discount rates being 7.56%,5.83%,5.60%,and 8.64%,for the hydropower,nuclear power,wind power,and photovoltaic power industries,respectively.In 2019,the four annual discount rates were highest for the photovoltaic power industry(8.66%),followed by hydropower(7.17%),wind power(5.72%),and nuclear power industry(5.26%).Forecasting to 2020 from the 2019 evaluation base period,the discount rates are 6.37%,5.00%,6.57%,and 9.05%for the photovoltaic power,hydropower,wind power,and nuclear power industries,respectively.Under the different capital structures,their forecasts for the photovoltaic power,hydropower,wind power,and nuclear power industries in 2020 are,respectively,within[4.35%,9.24%],[3.92%,7.10%],[4.58%,10.40%],[5.46%,14.81%].We also discussed more details on capital structure and forecast period of discount rates for China’s new energy power industries.Our analysis shows that it is necessary to establish a new energy power industry database and steadily promote the implementation of policies.

Speech on the Opening Ceremony of European Union-China "Energy Cooperation Industry Conference

Ladies, Gentlemen, Friends: Today, I feel greatly honoured to have opportunity to attend European Union-China《Energy Cooperation Industry Conference cosponsored by China State Science and Technology Commission and European Union of Federalists, held in Beijing. On this occasion, please allow me on behalf of the Ministry of Electric Power, People’s Republic of China, to express thanks for China State Science

New Energy Automobile is Drawing Industrial“Roadmap"

According to the White Paper on Development of 2016New Energy Automobile Electronic Industry released recently by the Electronic Science 8c Technology Intelligence Research Institute of Industry and Information Technology Ministry,China’s new energy automobile electronic industrial scale achieved nearly RMB 4.5 billion in 2015 and it is expected to reach RMB 23.7 billion by 2020 with

Analysis of China's Energy Industrial Development Trend in 2016

Nowadays,China has become the world’s largest energy producer,consumer and CO_2 emitter.Although the economic growth is slowing down,China still has a bigger portion in world’s energy demand increment for its larger economic volume and is an important endogenous factor of global energy market.

美国对华清洁能源产业竞争的态势与前景

With the development of global energy transition and carbon neutrality, the Biden administration attempts to enhance the economic competitiveness of the United States and its leadership in the field of international climate politics by promoting the development of clean energy industries such as solar photovoltaics, electric vehicles, and wind power generation. Toward this end, the Biden administration and the US Congress have increased support for the domestic clean energy industry through industrial policies and leveraged allied resources to achieve their agenda for this industry. A major target of the policies adopted by the Biden administration is competition with China. The approaches include politicizing the clean energy industry, linking investment access policies related to the clean energy industry with so-called national security considerations, and upgrading trade barriers against China. The competition orientation and policy measures of the US government in the field of clean energy industry will pose challenges to the international development of the clean energy industry in China. However, the US competition with China in the clean energy industry will be restricted due to domestic partisan politics and other factors. Thus, the potential exists for cooperation between China and the United States in the clean energy industry, which could create favorable conditions for the two countries to build a mutually beneficial cooperative relationship.

Analysis of China's New Energy Vehicle Battery Industry

As a strategic emerging industry,the new energy vehicle industry has been a key industry in China in recent years and has great significance for China to realize industrial transformation and upgrading,and seize the commanding heights of international competition.In particular,the industry has been vigorously boosted by the introduction of the subsidy policy in 2014 and has achieved faster development.Today,China’s new

Pike Research: New Energy Greatly Promotes China's Rare Earth Industry

According to the latest report of Pike Research,the demand for rare earth in the new energy technology field will break the mark of 12,000 tons to reach 12,920 tons in 2017 from about 9,000 tons in 2011,indicating rapid development of China’s rare earth hydrogen storage material industry.

价值链视角下中国新能源汽车产业网络及其机制

Based on the data of listed companies in the core industry chain of China's new energy vehicles in 2015 and 2021,this paper constructs their industrial network from the perspective of the value chain,and uses methods such as social network and negative binomial regression model to study the characteristics,evolution,differences,and formation mechanisms of different value chain networks.The results show that:(1)R&D-oriented,production-oriented,and service-oriented networks share several common features:These networks are simultaneously expanding in scale and transitioning towards more efficient“small world”network;The degree distribution in these networks follows a power-law distribution,indicating a scale-free network structure;There is a decrease in the power-law exponent of network's degree distribution,indicating an increase in network heterogeneity.Furthermore,there is a significant positive correlation between the degrees of nodes in networks with diverse value chains,suggesting that the same node holds a similar level of significance across different networks.(2)The number of power-prestige,power and prestige nodes increases in the networks of all value chain segments,except in the service-oriented network,where there are no power nodes.In each value chain network,these nodes have different agglomeration directions:In R&D-oriented network,the nodes tend to cluster around headquarters and high-level cities.In contrast,service-oriented network shows a concentration of nodes in municipalities,sub-provincial and provincial capitals.Similarly,production-oriented network demonstrates a clustering of nodes in traditional production bases.(3)Different value-added segments of industry form different types of agglomeration in pursuit of different factor endowments and agglomeration effect,and form the spatial structure of the strongest connection industrial network with different characteristics.The R&D-oriented networks have always been an integrated and closely connected multiple core-periphery structure community with the influence of social,technological and geographical proximities;Transformation of service-oriented network from an integrated and closely connected multiple core-periphery structure community to a multiple core-semi-periphery-periphery structure community with the influence of social,geographical and institutional proximities;Transformation of production-oriented network from the partially integrated and localized core-periphery structure community to the more decentralized multiple independent core-periphery structure community with the influence of the social,institutional of administrative boundaries and geographical proximities.

An input-output model for energy accounting and analysis of industrial production processes： a case study of an integrated steel plant

To promote sustainability, it has become increasingly vital to properly account material and energy flows in industrial production processes. Therefore, a generic process-level input-output （IO） model was developed to provide an integrated energy （material） accounting and analysis approach for industrial production processes. By extending the existing processlevel IO models, the production, usage, export and loss of by-products were explicitly considered in the proposed IO model. Moreover, the by-products allocation procedures were incorporated into the proposed IO model to reflect individual contributions of products to energy consumption. Finally, the proposed model enabled calculating embodied energy of main products and total energy consumption under hierarchical accounting scope. Plant managers, energy management consultants, governmental officials and academic researchers could use this input-output model to account material and energy flows, thus calculating energy consumption indicators of a production process with their specific system boundary requirements. The accounting results could be further used for energy labeling, identifying bottlenecks of production activities, evaluating industrial symbiosis effects, improving materials and energy utilization efficiency, etc. The model could also be used as a planning tool to determine the effect that a particular change of technology and supply chains may have on the industrial production processes. The proposed model was tested and applied in a real integrated steel mill, which also provided the reference results for related researches. At last, some concepts, computational issues and limi- tations of the proposed model were discussed.

Regional characteristics of industrial energy efficiency in China： application of stochastic frontier analysis method

This paper analyzed regional industrial energy efficiency in China with Total-Factor Energy Efficiency （TFEE）. The East region has the best energy efficiency and the Central and the West regions stand as the second and the third respectively. However, it is found that industrial energy efficiency of all regions increased from 1998 to 2006. This result is consistent with level of economic development of every region. The industries of all provinces in China are not yet at the frontier efficiency position, therefore, to the frontier as target, their technol- ogy levels and production processes should be adjusted accordingly. Compared with the conventional energy efficiency, the inverse of energy intensity, which is defined as the ratio of actual output to energy input, is regarded as Single-Factor Energy Efficiency （SFEE） index. Although TFEE ranks are not changed for each region, they are different for each province. The comparative result also shows that the substitution among inputs （labor, capital stock, and energy） to produce the output is significant. The SFEE scores could be over-estimated if energy is taken as the single input in the production. Finally, we identified determining factors affecting industrial energy efficiency using Tobit model. The results indicate that an increase of per capita Gross Domestic Product （GDP）, the percentage of output value of industry invested by Hong Kong, Macao, Taiwan and abroad, energy price and investment of scientific and technological activities for industry could be possible contributors and drivers to the industrial energy efficiency. However, increasing of heavy industry will lead to worse industrial energy efficiency.

A 3 Billion Yuan Lithium Battery New Energy Vehicle Industrial Cluster Project Landed in Daye

On December 24,a new industrial partner entered the friend circle of new energy vehicle industrial cluster in Daye;a new material project with a total investment of 3 billion yuan held ground-breaking ceremony,signaling its formal landing in Daye.This Project is invested and constructed by Hubei Zhongxing New Advanced Material Co.,Ltd,the Project involves total investment of

Typical load profile-supported convolutional neural network for short-term load forecasting in the industrial sector

This paper investigates how existing forecasting models can be enhanced to accurately forecast the electric load at factory level,enabling industrial companies to shift consumption to times of low energy costs.The model architecture must outperform state-of-the-art models and be sufficiently robust for use in multiple factories with low effort for specific applications.Moreover,this work focuses on the processing of high-resolution input data available almost in real time from multiple submeters after the main meters.The theory of load forecasting and related works are summarized in a first step including the requirements of forecasting models applied at factory level.Based on existing models,a new hybrid machine-learning model is proposed,combining a decision tree-based typical load profiler with a convolutional neural network that extracts features from multidimensional endogenous inputs with measurements of the preceding two weeks for multi-step-ahead load forecasts updated almost in real time.Furthermore,a multi-model approach is presented for calculating bottom-up forecasts with submeter data aggregated to a main-meter forecast.In a case study,the forecasting accuracy of the hybrid model is compared to both base models and a seasonal naïve model calculating the load forecasts for three factories.The results indicate that the proposed typical-load-profile-supported convolutional neural network for all three factories achieves the lowest forecasting error.Furthermore,it is validated that a reduction in data transfer delay leads to better forecasts,as the forecasting accuracy is higher with near real time data than with a data transfer delay of one day.Thus,a model architecture is proposed for robust forecasting in digitalized factories.

Numerical insight into heat transfer enhancement by granules inner-migration in a moving bed:A performance comparison between slanted-stick and plow-shaped agitator

Waste heat recovery of high-temperature granules is one of the most promising sustainable energy supply and carbon reduction ways for industry.A moving bed indirect heat exchanger(MBIHE)with inner-migration was proposed for granular heat recovery.Granule migration and the enhanced heat transfer induced by two types of agitators(i.e.,agitator with slanted stick A_(s)and with plow-shaped surface A_(p))in the MBIHE are analyzed based on DEM coupled with CFD.Owing to the effective agitation,the average heat transfer coefficient in the granule side is enhanced to∼3 times compared to that without agitation.The heat recovery efficiency in the moving bed reached more than 70%with the agitations of either A_(s)or A_(p).The heat efficiency of A_(s)is∼7%higher than that of A_(p),but with at least 60%greater rotational torque.To ensure reliable agitation,the A_(p)is suggested to be adopted in the MBIHE to induce granule migration.